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RAG

Retrieval-Augmented Generation (RAG) is a task that combines the strengths of both retrieval-based models and generation-based models. In this approach, a retrieval system selects relevant documents or passages from a large corpus, and a generation model, typically a neural language model, uses the retrieved information to generate a response. This method enhances the accuracy and coherence of generated text, especially in tasks requiring detailed knowledge or long context handling.

RAG is particularly useful in open-domain question answering, knowledge-grounded dialogue, and summarization tasks. The retrieval step helps the model to access and incorporate external information, making it less reliant on memorized knowledge and better suited for generating responses based on the latest or domain-specific information.

The performance of RAG systems is usually measured using metrics such as precision, recall, F1 score, BLEU score, and exact match. Some popular datasets for evaluating RAG models include Natural Questions, MS MARCO, TriviaQA, and SQuAD.

Papers

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Showing 1676–1700 of 2111 papers

TitleStatusHype
Graph-Augmented LLMs for Personalized Health Insights: A Case Study in Sleep Analysis—0
On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models—0
Context-augmented Retrieval: A Novel Framework for Fast Information Retrieval based Response Generation using Large Language Model—0
Ragnarök: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation TrackCode1
Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization—0
FS-RAG: A Frame Semantics Based Approach for Improved Factual Accuracy in Large Language ModelsCode0
Pistis-RAG: Enhancing Retrieval-Augmented Generation with Human Feedback—0
Towards Retrieval Augmented Generation over Large Video Libraries—0
Retrieve-Plan-Generation: An Iterative Planning and Answering Framework for Knowledge-Intensive LLM GenerationCode0
TemPrompt: Multi-Task Prompt Learning for Temporal Relation Extraction in RAG-based Crowdsourcing Systems—0
UDA: A Benchmark Suite for Retrieval Augmented Generation in Real-world Document AnalysisCode1
Integrating Knowledge Retrieval and Large Language Models for Clinical Report Correction—0
A Tale of Trust and Accuracy: Base vs. Instruct LLMs in RAG SystemsCode0
LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs—0
Evaluating RAG-Fusion with RAGElo: an Automated Elo-based FrameworkCode2
TTQA-RS- A break-down prompting approach for Multi-hop Table-Text Question Answering with Reasoning and Summarization—0
Relation Extraction with Fine-Tuned Large Language Models in Retrieval Augmented Generation Frameworks—0
CodeRAG-Bench: Can Retrieval Augment Code Generation?Code2
QPaug: Question and Passage Augmentation for Open-Domain Question Answering of LLMsCode0
DIRAS: Efficient LLM Annotation of Document Relevance in Retrieval Augmented GenerationCode0
Improving Zero-shot LLM Re-Ranker with Risk Minimization—0
Model Internals-based Answer Attribution for Trustworthy Retrieval-Augmented GenerationCode1
FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answering—0
InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized RationalesCode2
Multi-Meta-RAG: Improving RAG for Multi-Hop Queries using Database Filtering with LLM-Extracted MetadataCode1
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